As it has been introduced in section  the main objective of this work is to construct a generative neural model to construct visually plausible document images given a reference layout. The strategy for augmenting data and its corresponding ground truth by synthetic images automatically generated has gained interest among the Computer Vision community. Since they were proposed by Goodfellow in 2014, Generative Adversarial Networks (GANs) [1]} and subsequent variants have been a successful method to generate realistic images, ranging from handwritten digits to faces and natural scenes. A step forward which is a scientific challenge in the controlled generation of images in terms of the composition of objects and their arrangement.
Lake et al. in [2]} suggested a hierarchical generative model that can build whole objects from individual parts, it is shown to generate Omniglot characters as a composition of the strokes. Zhao et al. [3]} proposed a model that can generate a set of realistic images with objects in the desired locations, given a reference spatial distribution of bounding boxes and object labels. Our work has been inspired in this work.
